Extracting Patient Information from an Electronic Medical Record

ABSTRACT

A mechanism is provided in a data processing system comprising at least one processor and at least one memory comprising instructions, which are executed by the at least one processor and configure the processor to implement a patient information extractor. The patient information extractor receives a query specification for executing a query on a patient electronic medical record (EMR). The query specification provides parameters indicating a methodology for extracting search results from the patient EMR. The patient information extractor retrieves the patient EMR from a patient registry. The patient information extractor automatically executes the query specification on the retrieved patient EMR to thereby extract the search results from the patient EMR in accordance with the parameters of the query specification. The patient information extractor automatically processes the extracted search results to generate a patient indicator value. The patient indicator value represents an answer to a question about the patient. A patient evaluation operation is performed based on the patient indicator value.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for extractingpatient information from an electronic medical record (EMR).

A decision support system (DSS) is a computer-based information systemthat supports business or organizational decision-making activities.DSSs serve the management, operations, and planning levels of anorganization (usually mid and higher management) and help people makedecisions about problems that may be rapidly changing and not easilyspecified in advance—i.e. Unstructured and Semi-Structured decisionproblems. Decision support systems can be either fully computerized,human-powered or a combination of both.

A clinical decision support system (CDSS) is a health informationtechnology system that is designed to provide physicians and otherhealth professionals with clinical decision support (CDS), that is,assistance with clinical decision-making tasks. CDSSs constitute a majortopic in artificial intelligence in medicine.

A common purpose of modern CDSS is to assist clinicians at the point ofcare. This means that clinicians interact with a CDSS to help toanalyze, and reach a diagnosis based on, patient data. In the earlydays, CDSSs were conceived of as being used to literally make decisionsfor the clinician. The clinician would input the information and waitfor the CDSS to output the “right” choice and the clinician would simplyact on that output. However, the modern methodology of using CDSSs toassist means that the clinician interacts with the CDSS, utilizing boththeir own knowledge and the CDSS, to make a better analysis of thepatient's data than either human or CDSS could make on their own.Typically, a CDSS makes suggestions for the clinician to look through,and the clinician is expected to pick out useful information from thepresented results and discount erroneous CDSS suggestions.

An example of how a CDSS might be used by a clinician is a specific typeof Clinical Decision Support System, a DDSS (Diagnosis Decision SupportSystems). A DDSS requests some of the patients data and in response,proposes a set of appropriate diagnoses. The doctor then takes theoutput of the DDSS and determines which diagnoses might be relevant andwhich are not, and if necessary orders further tests to narrow down thediagnosis.

Another important classification of a CDSS is based on the timing of itsuse. Doctors use these systems at point of care to help them as they aredealing with a patient, with the timing of use being eitherpre-diagnosis, during diagnosis, or post diagnosis. Pre-diagnosis CDSSsystems are used to help the physician prepare the diagnoses. CDSS usedduring diagnosis help review and filter the physician's preliminarydiagnostic choices to improve their final results. Post-diagnosis CDSSsystems are used to mine data to derive connections between patients andtheir past medical history and clinical research to predict futureevents. It has been claimed that decision support will begin to replaceclinicians in common tasks in the future.

Another approach, used by the National Health Service in England, is touse a DDSS (either operated by the patient or by a phone operative whois not medically-trained) to triage medical conditions out of hours bysuggesting a suitable next step to the patient (e.g., call an ambulance,or see a general practitioner on the next working day). The suggestion,which may be disregarded by either the patient or the phone operative ifcommon sense or caution suggests otherwise, is based on the knowninformation and an implicit conclusion about what the worst-casediagnosis is likely to be.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided in a dataprocessing system comprising at least one processor and at least onememory, the at least one memory comprising instructions which areexecuted by the at least one processor and configure the processor toimplement a patient information extractor. The method comprisesreceiving, by the patient information extractor, a query specificationfor executing a query on a patient electronic medical record (EMR). Thequery specification provides parameters indicating a methodology forextracting search results from the patient EMR. The method furthercomprises retrieving, by the patient information extractor, the patientEMR from a patient registry. The method further comprises automaticallyexecuting, by the patient information extractor, the query specificationon the retrieved patient EMR to thereby extract the search results fromthe patient EMR in accordance with the parameters of the queryspecification. The method further comprises automatically processing, bythe patient information extractor, the extracted search results togenerate a patient indicator value. The patient indicator valuerepresents an answer to a question about the patient. The method furthercomprises performing a patient evaluation operation based on the patientindicator value.

In other illustrative embodiments, a computer program product comprisinga computer usable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment;

FIG. 4 is a block diagram of a patient information extractor inaccordance with an illustrative embodiment;

FIG. 5 shows an example query specification in accordance with anillustrative embodiment; and

FIG. 6 is a flowchart illustrating operation of a patient informationextractor in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

In this disclosure, the term “patient indicator” refers to a value,which may be Boolean, numeric, date, categorical, or any other type,that represents the answer to a question about the patient, thepatient's health, or the patient's medical, social, or family historypresent in an electronic medical record (EMR). Typical such questionsinclude:

What is the patient's age?

What is the patient's ethnicity?

Does the patient have a family history of cancer?

Has the patient been on tamoxifen within the last year?

Did the patient ever have blood clots following surgery?

What kind of hysterectomy did the patient have?

When was the patient's last colonoscopy?

Several use cases require the gathering of collections of suchindicators for one or many patients. These include retrospective chartreview for clinical research, clinical trial matching (i.e., selectingsuitable participants for a trial), quality assurance in health caresystems, outcome prediction research, and epidemiology studies.Currently, these indicators are extracted from an EMR manually. Thisprocess is time-consuming, requires trained individuals, and is subjectto human error. The illustrative embodiments provide an automaticmechanism for extracting patient indicators from an electronic medicalrecord.

An indicator question falls into one of a small number of classes:yes/no, temporal, categorical, etc. The illustrative embodiments providea dedicated search engine for each of these classes, which given one ormore search terms and possible constraints can generate a list of searchresults. The illustrative embodiments also provide post-processors thattake these search results and any constraints not expressible to thesearch engine, perform filtering and sorting, and extract indicatorvalues.

All of the parameters for this search and extraction process can be laidout in a machine-readable attribute-value format, such as theJavaScript™ Object Notation (JSON), in a query specification datastructure or document. The query specification contains entries for eachindicator of interest. The process is as follows: the user writes aquery specification for each patient indicator of interest; the queryspecification is run against the EMR, generating search results; thesearch results are post-processed to generate the indicator value; theindicator values are written out. This process may then be repeated forother patients, if desired. These indicators subsequently may be used todetermine whether the patient qualifies for a trial, to predict likelyfuture medical concerns, to assess whether appropriate care has beengiven, etc.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1-4 are provided hereafter as exampleenvironments in which aspects of the illustrative embodiments may beimplemented. It should be appreciated that FIGS. 1-4 are only examplesand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the presentinvention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

While the described embodiments describe a cognitive system, including aquestion-answering pipeline and an interactive session whereby users(doctors, technicians, healthcare workers, etc.) can query the systemfor information in order to help treat their patients, this is onepossible use case scenario in which the illustrative embodiments may beemployed; however, it is not necessarily the only or even the primaryway in which the invention may be used. For instance, one application ofthe invention is for what is called clinical trial matching. A medicalresearch team identifies a set of characteristics (such as middle-agedmen, non-smokers, family history of heart problems, being treated forhypertension) for which they need to find a set of patients with thesesame characteristics for a follow-up study. In one illustrativeembodiment, the medical research team would create the appropriate QuerySpecification which would be run against all of the EMRs they haveavailable. This would create a result record for each patient, which canbe scored to determine the degree of fit to their study needs. In thisembodiment, there may be no interactive session and no (immediate)treatment of patients.

Another application of the invention is to aid the user in determiningapplicability of candidate treatments for a particular patient. The useridentifies a set of patient-specific variables needed as evidential datato support or refute the applicability of a candidate treatment. Similarto the above example, the user would create the appropriate QuerySpecification to run against the specific EMR for the patient inquestion. The mechanism of this embodiment would return values for eachuser-specified variable, allowing the user to determine the mostappropriate treatment option for the specific patient.

In accordance with yet another illustrative embodiment, which involves aquestion-answering (QA) pipeline (such as the IBM Watson™ cognitivesystem), logical search engines (LSEs), which take query terms from thequery spec and produce hit lists, may include LSEs that accept queryterms in the form of a natural language question. In this case, an LSEitself may be a QA system. Therefore, in this example embodiment, the QAsystem is called by the mechanisms of the illustrative embodiments.

FIGS. 1-4 are directed to describing an example cognitive system forhealthcare applications (also referred to herein as a “healthcarecognitive system”) which implements a request processing pipeline, suchas a Question Answering (QA) pipeline (also referred to as aQuestion/Answer pipeline or Question and Answer pipeline) for example,request processing methodology, and request processing computer programproduct with which the mechanisms of the illustrative embodiments areimplemented. These requests may be provided as structure or unstructuredrequest messages, natural language questions, or any other suitableformat for requesting an operation to be performed by the healthcarecognitive system. As described in more detail hereafter, the particularhealthcare application that is implemented in the cognitive system ofthe present invention is a healthcare application for providing medicaltreatment recommendations for patients based on their specific featuresas obtained from various sources, e.g., patient electronic medicalrecords (EMRs), patient questionnaires, etc. In particular, themechanisms of the present invention provide a mechanism for extractingpatient information from an electronic medical record.

It should be appreciated that the healthcare cognitive system, whileshown as having a single request processing pipeline in the exampleshereafter, may in fact have multiple request processing pipelines. Eachrequest processing pipeline may be separately trained and/or configuredto process requests associated with different domains or be configuredto perform the same or different analysis on input requests (orquestions in implementations using a QA pipeline), depending on thedesired implementation. For example, in some cases, a first requestprocessing pipeline may be trained to operate on input requests directedto a first medical malady domain (e.g., various types of blood diseases)while another request processing pipeline may be trained to answer inputrequests in another medical malady domain (e.g., various types ofcancers). In other cases, for example, the request processing pipelinesmay be configured to provide different types of cognitive functions orsupport different types of healthcare applications, such as one requestprocessing pipeline being used for patient diagnosis, another requestprocessing pipeline being configured for medical treatmentrecommendation, another request processing pipeline being configured forpatient monitoring, etc.

Moreover, each request processing pipeline may have its own associatedcorpus or corpora that they ingest and operate on, e.g., one corpus forblood disease domain documents and another corpus for cancer diagnosticsdomain related documents in the above examples. In some cases, therequest processing pipelines may each operate on the same domain ofinput questions but may have different configurations, e.g., differentannotators or differently trained annotators, such that differentanalysis and potential answers are generated. The healthcare cognitivesystem may provide additional logic for routing input questions to theappropriate request processing pipeline, such as based on a determineddomain of the input request, combining and evaluating final resultsgenerated by the processing performed by multiple request processingpipelines, and other control and interaction logic that facilitates theutilization of multiple request processing pipelines.

As noted above, one type of request processing pipeline with which themechanisms of the illustrative embodiments may be utilized is a QuestionAnswering (QA) pipeline. The description of example embodiments of thepresent invention hereafter will utilize a QA pipeline as an example ofa request processing pipeline that may be augmented to includemechanisms in accordance with one or more illustrative embodiments. Itshould be appreciated that while the present invention will be describedin the context of the cognitive system implementing one or more QApipelines that operate on an input question, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may operate on requests that are not posed as“questions” but are formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system. For example, rather than askinga natural language question of “What diagnosis applies to patient P?”the cognitive system may instead receive a request of “generatediagnosis for patient P,” or the like. It should be appreciated that themechanisms of the QA system pipeline may operate on requests in asimilar manner to that of input natural language questions with minormodifications. In fact, in some cases, a request may be converted to anatural language question for processing by the QA system pipelines ifdesired for the particular implementation.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof these QA pipeline, or request processing pipeline, mechanisms of ahealthcare cognitive system with regard to providing a medical maladyindependent treatment recommendation system which may receive an inputquestion regarding the recommended treatment for a specific patient andmay utilize the QA pipeline mechanisms to evaluate patient informationand other medical information in one or more corpora of medicalinformation to determine the most appropriate treatment for the specificpatient. Evaluation of the patient may be performed by the cognitive orby external mechanism proved by the system builder.

Thus, it is important to first have an understanding of how cognitivesystems and question and answer creation in a cognitive systemimplementing a QA pipeline is implemented before describing how themechanisms of the illustrative embodiments are integrated in and augmentsuch cognitive systems and request processing pipeline, or QA pipeline,mechanisms. It should be appreciated that the mechanisms described inFIGS. 1-4 are only examples and are not intended to state or imply anylimitation with regard to the type of cognitive system mechanisms withwhich the illustrative embodiments are implemented. Many modificationsto the example cognitive system shown in FIGS. 1-4 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, for example, and machine learning logic, which may beprovided as specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.The logic of the cognitive system implements the cognitive operation(s),examples of which include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches, for example, medical diagnostic and treatment recommendations,and other types of recommendation generation, e.g., items of interest toa particular user, potential new contact recommendations, or the like.

The IBM Watson™ cognitive system is an example of one such cognitivesystem which can process human readable language and identify inferencesbetween text passages with human-like high accuracy at speeds far fasterthan human beings and on a larger scale. In general, such cognitivesystems are able to perform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypothesis    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situational awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system) and/or process requests which may or maynot be posed as natural language questions. The QA pipeline or system isan artificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area, e.g., financial domain, medical domain, legaldomain, etc., where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to cognitive system which implements theQA pipeline. The QA pipeline then answers the input questions using thecontent in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using Natural LanguageProcessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identify thesequestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a request processing pipeline 108,which in some embodiments may be a question answering (QA) pipeline, ina computer network 102. For purposes of the present description, it willbe assumed that the request processing pipeline 108 is implemented as aQA pipeline that operates on structured and/or unstructured requests inthe form of input questions. One example of a question processingoperation that may be used in conjunction with the principles describedherein is described in U.S. Patent Application Publication No.2011/0125734, which is herein incorporated by reference in its entirety.

The cognitive system 100 is implemented on one or more computing devices104 (comprising one or more processors and one or more memories, andpotentially any other computing device elements generally known in theart including buses, storage devices, communication interfaces, and thelike) connected to the computer network 102. The network 102 includesmultiple computing devices 104 in communication with each other and withother devices or components via one or more wired and/or wireless datacommunication links, where each communication link comprises one or moreof wires, routers, switches, transmitters, receivers, or the like. Thecognitive system 100 and network 102 enables question processing andanswer generation (QA) functionality for one or more cognitive systemusers via their respective computing devices 110-112. Other embodimentsof the cognitive system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receive inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, a corpus of electronicdocuments 106, cognitive system users, and/or other data and otherpossible sources of input. In one embodiment, some or all of the inputsto the cognitive system 100 are routed through the network 102. Thevarious computing devices 104 on the network 102 include access pointsfor content creators and QA system users. Some of the computing devices104 include devices for a database storing the corpus of data 106 (whichis shown as a separate entity in FIG. 1 for illustrative purposes only).Portions of the corpus of data 106 may also be provided on one or moreother network attached storage devices, in one or more databases, orother computing devices not explicitly shown in FIG. 1. The network 102includes local network connections and remote connections in variousembodiments, such that the cognitive system 100 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. QA system usersaccess the cognitive system 100 via a network connection or an Internetconnection to the network 102, and input questions to the cognitivesystem 100 that are answered by the content in the corpus of data 106.In one embodiment, the questions are formed using natural language. Thecognitive system 100 parses and interprets the question via a QApipeline 108, and provides a response to the cognitive system user,e.g., cognitive system user 110, containing one or more answers to thequestion. In some embodiments, the cognitive system 100 provides aresponse to users in a ranked list of candidate answers while in otherillustrative embodiments, the cognitive system 100 provides a singlefinal answer or a combination of a final answer and ranked listing ofother candidate answers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 106. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data106. The QA pipeline 108 will be described in greater detail hereafterwith regard to FIG. 4.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question which it then parses to extract the majorfeatures of the question, which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the QA pipeline of the IBM Watson™ cognitive system hasregarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is be repeated foreach of the candidate answers to generate ranked listing of candidateanswers which may then be presented to the user that submitted the inputquestion, or from which a final answer is selected and presented to theuser. More information about the QA pipeline of the IBM Watson™cognitive system may be obtained, for example, from the IBM Corporationwebsite, IBM Redbooks, and the like. For example, information about theQA pipeline of the IBM Watson™ cognitive system can be found in Yuan etal., “Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

As noted above, while the input to the cognitive system 100 from aclient device may be posed in the form of a natural language question,the illustrative embodiments are not limited to such. Rather, the inputquestion may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a healthcare based cognitive system, this analysis may involveprocessing patient medical records, medical guidance documentation fromone or more corpora, and the like, to provide a healthcare orientedcognitive system result.

In the context of the present invention, cognitive system 100 mayprovide a cognitive functionality for assisting with healthcare basedoperations. For example, depending upon the particular implementation,the healthcare based operations may comprise patient diagnostics,medical treatment recommendation systems, medical practice managementsystems, personal patient care plan generation and monitoring, patientelectronic medical record (EMR) evaluation for various purposes, such asfor identifying patients that are suitable for a medical trial or aparticular type of medical treatment, or the like. Thus, the cognitivesystem 100 may be a healthcare cognitive system 100 that operates in themedical or healthcare type domains and which may process requests forsuch healthcare operations via the request processing pipeline 108 inputas either structured or unstructured requests, natural language inputquestions, or the like. In one illustrative embodiment, the cognitivesystem 100 is a medical treatment recommendation system that analyzes apatient's EMR in relation to medical guidelines and other medicaldocumentation in a corpus of information generate a recommendation as tohow to treat a medical malady or medical condition of the patient.

In an illustrative embodiment, the cognitive system 100 implements apatient information extractor 120 for extracting patient informationfrom a patient's EMR. A system user 110, 112 may provide a queryspecification (QS) for a specific indicator and an identification of oneor more patient EMRs to patient information extractor 120, which runsthe query specification against the EMR and generates search results.Patient information extractor 120 performs post-processing on the searchresults to generate the indicator value and returns the indicator valueto the system user 110, 112.

In one embodiment, patient information extractor 120 provides one ormore logical search engines (LSE), which find matches for search termsin the EMR according to specific logic, and one or more search resultprocessors (SRP), which take the output of the LSEs, filter the resultsaccording to specified constraints, and return a result. Several LSEsmay be implemented by the same physical search engine. The LSEs and SRPsare shown in FIG. 6 and described in further detail below.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which implements an NLprocessing system 100 and NL system pipeline 108 augmented to includethe additional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 8®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System P® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment. The example diagram of FIG. 3 depicts an implementation of ahealthcare cognitive system 300 that is configured to provide medicaltreatment recommendations for patients. However, it should beappreciated that this is only an example implementation and otherhealthcare operations may be implemented in other embodiments of thehealthcare cognitive system 300 without departing from the spirit andscope of the present invention.

Moreover, it should be appreciated that while FIG. 3 depicts the patient302 and user 306 as human figures, the interactions with and betweenthese entities may be performed using computing devices, medicalequipment, and/or the like, such that entities 302 and 306 may in factbe computing devices, e.g., client computing devices. For example, theinteractions 304, 314, 316, and 330 between the patient 302 and the user306 may be performed orally, e.g., a doctor interviewing a patient, andmay involve the use of one or more medical instruments, monitoringdevices, or the like, to collect information that may be input to thehealthcare cognitive system 300 as patient attributes 318. Interactionsbetween the user 306 and the healthcare cognitive system 300 will beelectronic via a user computing device (not shown), such as a clientcomputing device 110 or 112 in FIG. 1, communicating with the healthcarecognitive system 300 via one or more data communication links andpotentially one or more data networks.

As shown in FIG. 3, in accordance with one illustrative embodiment, apatient 302 presents symptoms 304 of a medical malady or condition to auser 306, such as a healthcare practitioner, technician, or the like.The user 306 may interact with the patient 302 via a question 314 andresponse 316 exchange where the user gathers more information about thepatient 302, the symptoms 304, and the medical malady or condition ofthe patient 302. It should be appreciated that the questions/responsesmay in fact also represent the user 306 gathering information from thepatient 302 using various medical equipment, e.g., blood pressuremonitors, thermometers, wearable health and activity monitoring devicesassociated with the patient such as a FitBit™, a wearable heart monitor,or any other medical equipment that may monitor one or more medicalcharacteristics of the patient 302. In some cases such medical equipmentmay be medical equipment typically used in hospitals or medical centersto monitor vital signs and medical conditions of patients that arepresent in hospital beds for observation or medical treatment.

In response, the user 302 submits a request 308 to the healthcarecognitive system 300, such as via a user interface on a client computingdevice that is configured to allow users to submit requests to thehealthcare cognitive system 300 in a format that the healthcarecognitive system 300 can parse and process. The request 308 may include,or be accompanied with, information identifying patient attributes 318.These patient attributes 318 may include, for example, an identifier ofthe patient 302 from which patient EMRs 322 for the patient may beretrieved, demographic information about the patient, the symptoms 304,and other pertinent information obtained from the responses 316 to thequestions 314 or information obtained from medical equipment used tomonitor or gather data about the condition of the patient 302. Anyinformation about the patient 302 that may be relevant to a cognitiveevaluation of the patient by the healthcare cognitive system 300 may beincluded in the request 308 and/or patient attributes 318.

The healthcare cognitive system 300 provides a cognitive system that isspecifically configured to perform an implementation specific healthcareoriented cognitive operation. In the depicted example, this healthcareoriented cognitive operation is directed to providing a treatmentrecommendation 328 to the user 306 to assist the user 306 in treatingthe patient 302 based on their reported symptoms 304 and otherinformation gathered about the patient 302 via the question 314 andresponse 316 process and/or medical equipment monitoring/data gathering.The healthcare cognitive system 300 operates on the request 308 andpatient attributes 318 utilizing information gathered from the medicalcorpus and other source data 326, treatment guidance data 324, and thepatient EMRs 322 associated with the patient 302 to generate one or moretreatment recommendation 328. The treatment recommendations 328 may bepresented in a ranked ordering with associated supporting evidence,obtained from the patient attributes 318 and data sources 322-326,indicating the reasoning as to why the treatment recommendation 328 isbeing provided and why it is ranked in the manner that it is ranked.

For example, based on the request 308 and the patient attributes 318,the healthcare cognitive system 300 may operate on the request, such asby using a QA pipeline type processing as described herein, to parse therequest 308 and patient attributes 318 to determine what is beingrequested and the criteria upon which the request is to be generated asidentified by the patient attributes 318, and may perform variousoperations for generating queries that are sent to the data sources322-326 to retrieve data, generate candidate treatment recommendations(or answers to the input question), and score these candidate treatmentrecommendations based on supporting evidence found in the data sources322-326. In the depicted example, the patient EMRs 322 is a patientinformation repository that collects patient data from a variety ofsources, e.g., hospitals, laboratories, physicians' offices, healthinsurance companies, pharmacies, etc. The patient EMRs 322 store variousinformation about individual patients, such as patient 302, in a manner(structured, unstructured, or a mix of structured and unstructuredformats) that the information may be retrieved and processed by thehealthcare cognitive system 300. This patient information may comprisevarious demographic information about patients, personal contactinformation about patients, employment information, health insuranceinformation, laboratory reports, physician reports from office visits,hospital charts, historical information regarding previous diagnoses,symptoms, treatments, prescription information, etc. Based on anidentifier of the patient 302, the patient's corresponding EMRs 322 fromthis patient repository may be retrieved by the healthcare cognitivesystem 300 and searched/processed to generate treatment recommendations328.

The treatment guidance data 324 provides a knowledge base of medicalknowledge that is used to identify potential treatments for a patientbased on the patient's attributes 318 and historical informationpresented in the patient's EMRs 322. This treatment guidance data 324may be obtained from official treatment guidelines and policies issuedby medical authorities, e.g., the American Medical Association, may beobtained from widely accepted physician medical and reference texts,e.g., the Physician's Desk Reference, insurance company guidelines, orthe like. The treatment guidance data 324 may be provided in anysuitable form that may be ingested by the healthcare cognitive system300 including both structured and unstructured formats.

In some cases, such treatment guidance data 324 may be provided in theform of rules that indicate the criteria required to be present, and/orrequired not to be present, for the corresponding treatment to beapplicable to a particular patient for treating a particular symptom ormedical malady/condition. For example, the treatment guidance data 324may comprise a treatment recommendation rule that indicates that for atreatment of Decitabine, strict criteria for the use of such a treatmentis that the patient 302 is less than or equal to 60 years of age, hasacute myeloid leukemia (AML), and no evidence of cardiac disease. Thus,for a patient 302 that is 59 years of age, has AML, and does not haveany evidence in their patient attributes 318 or patient EMRs indicatingevidence of cardiac disease, the following conditions of the treatmentrule exist:

-   -   Age<=60 years=59 (MET);    -   Patient has AML=AML (MET); and    -   Cardiac Disease=false (MET)        Since all of the criteria of the treatment rule are met by the        specific information about this patient 302, then the treatment        of Decitabine is a candidate treatment for consideration for        this patient 302. However, if the patient had been 69 years old,        the first criterion would not have been met and the Decitabine        treatment would not be a candidate treatment for consideration        for this patient 302. Various potential treatment        recommendations may be evaluated by the healthcare cognitive        system 300 based on ingested treatment guidance data 324 to        identify subsets of candidate treatments for further        consideration by the healthcare cognitive system 300 by scoring        such candidate treatments based on evidential data obtained from        the patient EMRs 322 and medical corpus and other source data        326. While patient evaluation may be performed by cognitive        system 300 itself, patient evaluation may also be performed by        mechanisms outside cognitive system 300.

For example, data mining processes may be employed to mine the data insources 322 and 326 to identify evidential data supporting and/orrefuting the applicability of the candidate treatments to the particularpatient 302 as characterized by the patient's patient attributes 318 andEMRs 322. For example, for each of the criteria of the treatment rule,the results of the data mining provide a set of evidence that supportsgiving the treatment in the cases where the criterion is “MET” and incases where the criterion is “NOT MET.” The healthcare cognitive system300 processes the evidence in accordance with various cognitive logicalgorithms to generate a confidence score for each candidate treatmentrecommendation indicating a confidence that the corresponding candidatetreatment recommendation is valid for the patient 302. The candidatetreatment recommendations may then be ranked according to theirconfidence scores and presented to the user 306 as a ranked listing oftreatment recommendations 328. In some cases, only a highest ranked, orfinal answer, is returned as the treatment recommendation 328. Thetreatment recommendation 328 may be presented to the user 306 in amanner that the underlying evidence evaluated by the healthcarecognitive system 300 may be accessible, such as via a drilldowninterface, so that the user 306 may identify the reasons why thetreatment recommendation 328 is being provided by the healthcarecognitive system 300.

In accordance with the illustrative embodiments herein, the healthcarecognitive system 300 is augmented to operate with, implement, or includepatient information extractor 341 for extracting patient informationfrom electronic medical records. User 306 may provide a queryspecification (QS) 342 for a specific indicator and an identification ofone or more patient EMRs 322 to patient information extractor 341, whichruns the query specification against the EMR and generates searchresults. Patient information extractor 341 performs post-processing onthe search results to generate the indicator value and returns theindicator value in result record 343.

In one embodiment, patient information extractor 341 provides one ormore logical search engines (LSE), which find matches for search termsin the EMR according to specific logic, and one or more search resultprocessors (SRP), which take the output of the LSEs, filter the resultsaccording to specified constraints, and return a result. The LSEs andSRPs are shown in FIG. 6 and described in further detail below. Theoperation of patient information extractor 341 is described in furtherdetail below with reference to FIGS. 5-7.

While FIG. 3 is depicted with an interaction between the patient 302 anda user 306, which may be a healthcare practitioner such as a physician,nurse, physician's assistant, lab technician, or any other healthcareworker, for example, the illustrative embodiments do not require such.Rather, the patient 302 may interact directly with the healthcarecognitive system 300 without having to go through an interaction withthe user 306 and the user 306 may interact with the healthcare cognitivesystem 300 without having to interact with the patient 302. For example,in the first case, the patient 302 may be requesting 308 treatmentrecommendations 328 from the healthcare cognitive system 300 directlybased on the symptoms 304 provided by the patient 302 to the healthcarecognitive system 300. Moreover, the healthcare cognitive system 300 mayactually have logic for automatically posing questions 314 to thepatient 302 and receiving responses 316 from the patient 302 to assistwith data collection for generating treatment recommendations 328. Inthe latter case, the user 306 may operate based on only informationpreviously gathered and present in the patient EMR 322 by sending arequest 308 along with patient attributes 318 and obtaining treatmentrecommendations in response from the healthcare cognitive system 300.Thus, the depiction in FIG. 3 is only an example and should not beinterpreted as requiring the particular interactions depicted when manymodifications may be made without departing from the spirit and scope ofthe present invention.

As mentioned above, the healthcare cognitive system 300 may include arequest processing pipeline, such as request processing pipeline 108 inFIG. 1, which may be implemented, in some illustrative embodiments, as aQuestion Answering (QA) pipeline. The QA pipeline may receive an inputquestion, such as “what is the appropriate treatment for patient P?”, ora request, such as “diagnose and provide a treatment recommendation forpatient P.”

FIG. 4 is a block diagram of a patient information extractor inaccordance with an illustrative embodiment. Each patient is representedby an electronic medical record (EMR) 440. This record consists of (1) anumber of clinical notes 441, which are free-text descriptions ofpatient encounters with medical staff (e.g., office visits), surgicalprocedures, and interactions between medical professionals concerningthe patient, and (2) structured data 442 detailing ordered medications,tests and procedures, and patient demographic and possibly other staticinformation (e.g., lists of allergies). It is likely and desirable, butnot required for the purposes of this disclosure, that the EMR has beenprocessed by analytic tools, such as MetaMap, which can detect free-textmedical concepts and associate with them concepts in ontology, such asUnified Medical Language System (UM LS) dictionary, for example. TheUMLS is a compendium of many controlled vocabularies in the biomedicalsciences. It provides a mapping structure among these vocabularies and,thus, allows one to translate among the various terminology systems; itmay also be viewed as a comprehensive thesaurus and ontology ofbiomedical concepts. UMLS further provides facilities for naturallanguage processing. It is intended to be used mainly by developers ofsystems in medical informatics. Other useful but not required analytictools would be tools that determine the type of clinical note (e.g.,Office Visit, Operative Report, Telephone Encounter, PatientInstructions), and the sections within a note (e.g., Past MedicalHistory, Current Medications, Assessment and Plan).

Query specification processor 410 receives a query specification (QS)401. FIG. 5 shows an example query specification in accordance with anillustrative embodiment. The query specification (QS) for a specificindicator lists one or more query specification blocks (QSBs). Each QSBlists the search term(s) to be used, the LSE(s) to be used, a possibledate restriction, a possible note type restriction, a possible sectiontype restriction, a possible provider type restriction, a possibledepartment/specialty restriction, the search result processor (SRP) tobe used, and/or a mapping from search outputs to indicator outputs. Inthe example depicted in FIG. 5, the QS includes date restriction 501,QSBs 502, 503, 504, and note type 505. The QS specifies the LSE to useas “SemanticMatch,” and specifies the SRP to use as “SpecificIndicator.”QSB 502 searches for Bilateral salpingooophorectomy, QSB 503 searchesfor right or left salpingooophorectomy, and QSB 504 searches for nosalpingooophorectomy (none). Note type 505 specifies to search the“operativenote,” “ednote,” “consultnote,” and “procedurenote” note typesin the EMR.

Returning to FIG. 4, query specification processor 410 is given the QS401 for a given patient indicator and writes results to result record402. In one embodiment, result record 402 may be a spreadsheet with onerow per patient and one column per indicator variable. QS 401 consistsof one or more QSBs, which are processed in order. If a particular QSBproduces an outcome, it is written out and process stops for thisindicator. Otherwise, the next QSB is processed. If no QSB produces anoutcome, the null/default is written out.

In processing a QSB, query specification processor 410 calls thespecified logical search engine (LSE) 420 with the specified searchterm. A “literal” LSE finds instances of the search term. For example,given a search term “heart attack,” the LSE would match the term “heartattack.” A “literal” LSE is similar to a standard find function and mayor may not observe capitalization. A “semantic” LSE finds conceptualmatches. For example, a “semantic” LSE may match the search term “heartattack” with the matched term “myocardial infarction,” because bothterms are mapped to the same concept in UMLS. A “more specific” LSEfinds conceptual matches via “ISA” (is a) relations. In knowledgerepresentation, object-oriented programming and design, “ISA” (is_a oris-a) is a subsumption relationship between abstractions (e.g., types,classes), where one class A is a subclass of another class B (and so Bis a superclass of A). For example, a “more specific” LSE may match thesearch term “cancer” with “leukemia,” because in UMLS, leukemia ISAcancer. An “associative” LSE finds terms that co-occur in externalcorpora. For example, an “associative” LSE may match the search term“asthma” with the matched term “wheezing.” Standard technologies such aslatent semantic analysis (LSA) do this. A “logical” LSE finds conceptualmatches via relations other than ISA. For example, a “logical” LSE maymatch the search term “headache” with the matched term “Tylenol,”because Tylenol® TREATS headache, and match the search term “Tylenol” tothe matched term “acetaminophen,” because Tylenol is a BRAND_NAME_OFacetaminophen.

Each LSE 420 should produce a list of results (possibly empty) with thefollowing characteristics: the matched term, the date of the matchedterm (i.e., the date of the clinical note or structured entry containingthe match), the confidence score of the match, the note type (ifapplicable) the matched term was found in, the section type (ifapplicable) the matched term was found in, the type of provider (e.g.,physician, nurse, social worker) (if applicable) associated with thematch, and/or the department/specialty (if applicable) associated withthe match.

The LSE 420 searches EMR 440 and passes the search results to thespecified search results processor (SRP) 430, along with anyconstraints. The SRP 430 takes in a hit list from the LSE 420 and a setof parameters. SRP 430 outputs an indicator value. Some parameters arecommon across SRPs, e.g., MinimumCount, which is the number ofqualifying hit list entries that must be found for a positive result,and DateRange, which specifies a time window that qualifying hits mustfall within.

A “SpecificIndicator” SRP receives an indicator value as a parameter andperforms the following operation: If the hit list qualifies (by size anddate range), return the indicator value, else null. A “YesNo” SRPperforms the following operation: If the hit list qualifies, return Yes(or 1), else No (or 0). A “FirstLast” SRP receives a flag indicatingFirst/Last as a parameter and performs the following operation: Thequalifying hit list is sorted by date and the date of the first/lastentry is returned. A “Temporal” SRP can receive as parameters one or twodates, each date with a flag indicating before/after. With thisrevision, the SRP can return yes/no depending on if the match fallswithin two specified dates. An “AnswerType” SRP receives an answer typeindicating a property of the patient (e.g., age, weight) and performsthe following operation: either the answer is looked up in the datastructure part of the EMR, or a question-answering system is run on theclinical notes; the specified quantity is returned.

The SRP 430 filters the search results according to the constraints andreturns a possible indicator value to the query specification processor410. Query specification processor 410 writes the indicator value toresult record 402.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 6 is a flowchart illustrating operation of a patient informationextractor in accordance with an illustrative embodiment. Operationbegins for a current query specification and patient EMR (block 600),and the patient information extractor determines whether more queryspecification blocks (QSB) exist (block 601). Because a queryspecification has at least one QSB, the first iteration will result in adetermination that more QSB exist. The patient information extractorthen gets the next QSB in the query specification and extracts thespecified search term, logical search engine (LSE), and search resultprocessor (SRP) (block 602). The patient information extractor alsoextracts from the query specification one or more constraints, such as adate restriction, a note type restriction, a section type restriction, aprovider type restriction, or a department/specialty restriction.

The patient information extractor processes the search term with thespecified LSE to search the patient EMR and generate search results(block 603). The patient information extractor then processes the searchresults with the specified SRP to generate an indicator value (block604). The patient information extractor determines whether the indicatorvalue is null (i.e., the search results were empty or fail to meet theone or more constraints) (block 605). If the value is null, then thecurrent QSB did not result in generating an indicator value, andoperation returns to block 601 to determine whether more QSB exist. Thepatient information extractor then repeats blocks 602-605 for the nextQSB if one exists.

If the patient information extractor determines that the indicator valueis not null in block 605, then the current QSB generated a validindicator value, and the patient information extractor writes theindicator value to a file (e.g., result record 402 in FIG. 4) (block606). Thereafter, operation ends (block 607). Once a valid indicatorvalue is found, operation ends for the current query specification.Operation may return to block 600 for a next query specification and thesame or another patient EMR.

In response to the patient information extractor determining that thecurrent QSB does not generate a valid indicator value in block 605 andthat there are no more QSB (block 601: NO), operation proceeds to block608 where the patient information extractor sets the indicator value toa default value. The patient information extractor then writes theindicator value to file (block 606), and operation ends (block 607). Asstated above, operation may return to block 600 for a next queryspecification and the same or another patient EMR.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments provide a mechanism for extractingpatient information from an electronic medical record. There are severalcurrent use cases that would benefit from extracting and collectingpatient information from an EMR for further evaluation or analysis,including: clinical trial matching, retrospective chart review, outcomeprediction, and quality assurance. Manual extraction of patientinformation from EMRs takes considerable time by trained individuals andis subject to human error. The mechanism of the illustrative embodimentsuses automated extraction of patient indicators using a logical searchengine on structured and unstructured data within a patient medicalrecord. The illustrative embodiments is generalized to be applicable forall activities requiring data abstraction from patient medical records.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisingat least one processor and at least one memory, the at least one memorycomprising instructions which are executed by the at least one processorand configure the processor to implement a patient informationextractor, wherein the method comprises: receiving, by the patientinformation extractor, a query specification for executing a query on apatient electronic medical record (EMR), wherein the query specificationprovides parameters indicating a methodology for extracting searchresults from the patient EMR; retrieving, by the patient informationextractor, the patient EMR from a patient registry; automaticallyexecuting, by the patient information extractor, the query specificationon the retrieved patient EMR to thereby extract the search results fromthe patient EMR in accordance with the parameters of the queryspecification; automatically processing, by the patient informationextractor, the extracted search results to generate a patient indicatorvalue, wherein the patient indicator value represents an answer to aquestion about the patient; and performing a patient evaluationoperation based on the patient indicator value.
 2. The method of claim1, wherein the query specification comprises a plurality of queryspecification blocks, wherein automatically executing the queryspecification on the retrieved patient EMR comprises processing theplurality of query specification blocks in order until a queryspecification block generates a valid patient indicator value.
 3. Themethod of claim 1, wherein the query specification specifies a searchterm, a logical search engine (LSE), and a search results processor(SRP), wherein automatically executing the query specification on theretrieved patient EMR comprises executing the LSE to search theretrieved patient EMR to search for the specified search term andgenerate the search results, and wherein automatically processing theextracted search results comprises executing the SRP to filter thesearch results according to specified constraints and return the patientindicator value.
 4. The method of claim 3, wherein the specified LSE isselected from the group consisting of: a literal LSE that findsinstances of the search term in the patient EMR; a semantic LSE thatfinds conceptual matches of the search term in the patient EMR; aconceptually more specific LSE that finds conceptual matches of thesearch term in the patient EMR via ISA relations; an associative LSEthat finds terms that co-occur in external corpora; and a logical LSEthat finds conceptual matches via relations other than ISA.
 5. Themethod of claim 3, wherein the specified SRP is selected from the groupconsisting of: a SpecificIndicator SRP, a YesNo SRP, a FirstLast SRP, aTemporal SRP, and an AnswerType SRP.
 6. The method of claim 1, whereinthe patient evaluation operation is one of a retrospective patientmedical chart review operation for clinical research, a clinical trialmatching operation, a quality assurance operation for a health caresystem, a medical outcome prediction operation, or an epidemiologystudies operation.
 7. The method of claim 1, wherein the patientindicator value is one of a Boolean value, a numeric value, a temporalvalue, or a categorical value.
 8. The method of claim 1, wherein thepatient EMR comprises unstructured natural language content andstructured information content detailing at least one of encounters witha corresponding patient, procedures performed on the patient,interactions with medical practitioners concerning the patient,medications associated with the patient, results of medical tests andprocedures, patient demographic information, or static medical conditioninformation.
 9. The method of claim 8, wherein automatically executingthe query specification on the patient EMR comprises performing naturallanguage processing operations on the unstructured natural languagecontent to extract the search results in accordance with the parametersspecified in the query specification.
 10. The method of claim 1, whereinautomatically executing the query specification on the patient EMRcomprises utilizing one or more logical search engines to extract thesearch results, wherein the search results of the one or more logicalsearch engines comprises at least one matched natural language term, andwherein for each matched natural language term in the at least onematched natural language term, at least one of: a date associated withmatched natural language term; a confidence score associated with thematched natural language term; a type of clinical note in which thematched natural language term was found; a type of section of thepatient EMR in which the matched natural language term was found; a typeof medical practitioner that is a source of the matched natural languageterm in the patient EMR; or a medical department or medical specialtyassociated with the matched natural language term.
 11. The method ofclaim 1, wherein the parameters specified in the query specificationcomprise at least one parameter selected from the following: naturallanguage terms to be searched for in a natural language searchoperation; specific logical search engines in a plurality of logicalsearch engines to utilize to execute the query specification on apatient EMR; a date restriction for the search results; a clinical notetype restriction for the search results; a patient EMR section typerestriction for the search results; a medical practitioner sourcerestriction for the search results; a medical department or medicalspecialty type restriction for the search results; a specific searchresult processor in a plurality of search result processors, each ofwhich generates a different type of patient indicator value, forgenerating the patient indicator value based on the search results; or amapping from the search results to the patient indicator value.
 12. Acomputer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program comprises instructions, which when executed on aprocessor of a computing device causes the computing device to implementa patient information extractor, wherein the computer readable programcauses the computing device to: receive, by the patient informationextractor, a query specification for executing a query on a patientelectronic medical record (EMR), wherein the query specificationprovides parameters indicating a methodology for extracting searchresults from the patient EMR; retrieve, by the patient informationextractor, the patient EMR from a patient registry; automaticallyexecute, by the patient information extractor, the query specificationon the retrieved patient EMR to thereby extract the search results fromthe patient EMR in accordance with the parameters of the queryspecification; automatically process, by the patient informationextractor, the extracted search results to generate a patient indicatorvalue, wherein the patient indicator value represents an answer to aquestion about the patient; and perform a patient evaluation operationbased on the patient indicator value.
 13. The computer program productof claim 12, wherein the query specification comprises a plurality ofquery specification blocks, wherein automatically executing the queryspecification on the retrieved patient EMR comprises processing theplurality of query specification blocks in order until a queryspecification block generates a valid patient indicator value.
 14. Thecomputer program product of claim 12, wherein the query specificationspecifies a search term, a logical search engine (LSE), and a searchresults processor (SRP), wherein automatically executing the queryspecification on the retrieved patient EMR comprises executing the LSEto search the retrieved patient EMR to search for the specified searchterm and generate the search results, and wherein automaticallyprocessing the extracted search results comprises executing the SRP tofilter the search results according to specified constraints and returnthe patient indicator value.
 15. The computer program product of claim12, wherein the patient indicator value is one of a Boolean value, anumeric value, a temporal value, or a categorical value.
 16. Thecomputer program product of claim 12, wherein the patient EMR comprisesunstructured natural language content and structured information contentdetailing at least one of encounters with a corresponding patient,procedures performed on the patient, interactions with medicalpractitioners concerning the patient, medications associated with thepatient, results of medical tests and procedures, patient demographicinformation, or static medical condition information.
 17. A computingdevice comprising: a processor; and a memory coupled to the processor,wherein the memory comprises instructions, which when executed on aprocessor of a computing device causes the computing device to implementa patient information extractor, wherein the instructions cause theprocessor to: receive, by the patient information extractor, a queryspecification for executing a query on a patient electronic medicalrecord (EMR), wherein the query specification provides parametersindicating a methodology for extracting search results from the patientEMR; retrieve, by the patient information extractor, the patient EMRfrom a patient registry; automatically execute, by the patientinformation extractor, the query specification on the retrieved patientEMR to thereby extract the search results from the patient EMR inaccordance with the parameters of the query specification; automaticallyprocess, by the patient information extractor, the extracted searchresults to generate a patient indicator value, wherein the patientindicator value represents an answer to a question about the patient;and perform a patient evaluation operation based on the patientindicator value.
 18. The computing device of claim 17, wherein the queryspecification comprises a plurality of query specification blocks,wherein automatically executing the query specification on the retrievedpatient EMR comprises processing the plurality of query specificationblocks in order until a query specification block generates a validpatient indicator value.
 19. The computing device of claim 17, whereinthe query specification specifies a search term, a logical search engine(LSE), and a search results processor (SRP), wherein automaticallyexecuting the query specification on the retrieved patient EMR comprisesexecuting the LSE to search the retrieved patient EMR to search for thespecified search term and generate the search results, and whereinautomatically processing the extracted search results comprisesexecuting the SRP to filter the search results according to specifiedconstraints and return the patient indicator value.
 20. The computingdevice of claim 17, wherein the patient EMR comprises unstructurednatural language content and structured information content detailing atleast one of encounters with a corresponding patient, proceduresperformed on the patient, interactions with medical practitionersconcerning the patient, medications associated with the patient, resultsof medical tests and procedures, patient demographic information, orstatic medical condition information.